/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package com.zhaohg.spark.examples.mllib;

// $example on$

import org.apache.spark.SparkConf;
import org.apache.spark.SparkContext;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.mllib.classification.LogisticRegressionModel;
import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS;
import org.apache.spark.mllib.evaluation.MulticlassMetrics;
import org.apache.spark.mllib.linalg.Matrix;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.util.MLUtils;
import scala.Tuple2;

// $example off$

public class JavaMulticlassClassificationMetricsExample {
    public static void main(String[] args) {
        SparkConf conf = new SparkConf().setAppName("Multi class Classification Metrics Example");
        SparkContext sc = new SparkContext(conf);
        // $example on$
        String path = "data/mllib/sample_multiclass_classification_data.txt";
        JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc, path).toJavaRDD();
        
        // Split initial RDD into two... [60% training data, 40% testing data].
        JavaRDD<LabeledPoint>[] splits = data.randomSplit(new double[]{0.6, 0.4}, 11L);
        JavaRDD<LabeledPoint> training = splits[0].cache();
        JavaRDD<LabeledPoint> test = splits[1];
        
        // Run training algorithm to build the model.
        final LogisticRegressionModel model = new LogisticRegressionWithLBFGS()
                .setNumClasses(3)
                .run(training.rdd());
        
        // Compute raw scores on the demo3 set.
        JavaRDD<Tuple2<Object, Object>> predictionAndLabels = test.map(
                new Function<LabeledPoint, Tuple2<Object, Object>>() {
                    public Tuple2<Object, Object> call(LabeledPoint p) {
                        Double prediction = model.predict(p.features());
                        return new Tuple2<Object, Object>(prediction, p.label());
                    }
                }
        );
        
        // Get evaluation metrics.
        MulticlassMetrics metrics = new MulticlassMetrics(predictionAndLabels.rdd());
        
        // Confusion matrix
        Matrix confusion = metrics.confusionMatrix();
        System.out.println("Confusion matrix: \n" + confusion);
        
        // Overall statistics
        System.out.println("Accuracy = " + metrics.accuracy());
        
        // Stats by labels
        for (int i = 0; i < metrics.labels().length; i++) {
            System.out.format("Class %f precision = %f\n", metrics.labels()[i], metrics.precision(
                    metrics.labels()[i]));
            System.out.format("Class %f recall = %f\n", metrics.labels()[i], metrics.recall(
                    metrics.labels()[i]));
            System.out.format("Class %f F1 score = %f\n", metrics.labels()[i], metrics.fMeasure(
                    metrics.labels()[i]));
        }
        
        //Weighted stats
        System.out.format("Weighted precision = %f\n", metrics.weightedPrecision());
        System.out.format("Weighted recall = %f\n", metrics.weightedRecall());
        System.out.format("Weighted F1 score = %f\n", metrics.weightedFMeasure());
        System.out.format("Weighted false positive rate = %f\n", metrics.weightedFalsePositiveRate());
        
        // Save and load model
        model.save(sc, "target/tmp/LogisticRegressionModel");
        LogisticRegressionModel sameModel = LogisticRegressionModel.load(sc,
                "target/tmp/LogisticRegressionModel");
        // $example off$
    }
}
